DESCRIPTION: The long-term goal of this proposal is to use the electronic medical record, including narrative text, to understand and encode the process of care for individual patients in order to improve patient safety. Achieving this goal has the potential to help detect adverse events, and to differentiate medical errors from appropriately tailored care. The specific aims for this proposal are as follows: 1) To understand and encode the process of care for individual patients using data in the electronic medical record, including narrative text. 2) To use a more detailed understanding of patients' processes of care to improve automated adverse event detection. 3) To match processes of care for individual patients against accepted care pathways in order to identify discrepancies. We will capitalize on three core technologies that are in active use by clinicians and researchers in our busy clinical setting: 1) a Web-based clinical information system and its associated clinical data repository (WebCIS), 2) a full medical language parser (MedLEE), and 3) a semi-structured, electronic physician documentation system built by the applicant specifically to support this project (eNote). Methods will include evaluating the performance (sensitivity, specificity and positive predictive value) of our system, DETER+MINE (DETecting ERrors Mining Narrative Electronically), to model the care process and detect adverse events and pathway deviations. We will utilize explicit process criteria and manual, retrospective chart review as a gold standard. This research is intended to provide proof of concept that combining natural language processing of clinical narrative with traditional sources of coded data is required for effective screening with automated defection systems. This approach has the potential to impact significantly on our ability to detect and investigate medical errors, adverse medical events, and pathway deviations by reducing reliance on costly and slow manual chart reviews.